# model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
    type='EncoderDecoder',
    backbone=dict(type='UniFormer',
                  embed_dim=[64, 128, 320, 512],
                  layers=[3, 4, 8, 3],
                  head_dim=64,
                  mlp_ratio=4.,
                  qkv_bias=True,
                  drop_rate=0.,
                  attn_drop_rate=0.,
                  drop_path_rate=0.1),
    neck=dict(type='FPN',
              in_channels=[64, 128, 320, 512],
              out_channels=256,
              num_outs=4),
    decode_head=dict(type='FPNHead',
                     in_channels=[256, 256, 256, 256],
                     in_index=[0, 1, 2, 3],
                     feature_strides=[4, 8, 16, 32],
                     channels=128,
                     dropout_ratio=0.1,
                     num_classes=150,
                     norm_cfg=norm_cfg,
                     align_corners=False,
                     loss_decode=dict(type='CrossEntropyLoss',
                                      use_sigmoid=False,
                                      loss_weight=1.0)),
    # model training and testing settings
    train_cfg=dict(),
    test_cfg=dict(mode='whole'))
